Automated cytology instruments are required to perform at a high degree of integrity. Errors caused by such biological specimen analysis systems may have potentially catastrophic effects. Therefore, it is highly desirable for automated biological specimen instruments to monitor themselves at regularly defined intervals. Monitoring may comprise a series of automated tests of each critical subsystem in the instrument.
Generally systems that perform automated scanning have five critical subsystems including illumination, image collection, autofocus, positioning and image processing systems.
In order to achieve a high degree of accuracy and repeatability, such automated biological specimen analysis systems tend to be complex, because they may contain thousands of components. Such complexity demands measuring a large number of parameters to accurately characterize the functionality of such a system. Typically, for instruments manufactured for applications like automated semiconductor wafer and photomask inspection, testing is done at the time of manufacture with stand alone oscilloscopes, spectrum analyzers displacement transducers and the like. Once the instruments pass such tests, they are shipped to customer sites. Typically, problems with these instruments are usually corrected only if they have a major impact on the efficacy of the device. Minor fluctuations in the efficacy of such instruments go unnoticed. It is common practice to run a suite of diagnostic tests only in accordance with regularly scheduled preventative maintenance programs. This may be typically scheduled several times a year. Unfortunately, the instruments are usually taken off line to run such tests and, as a result, productivity suffers. Until the scheduled tests are done, the untested instruments may produce faulty results for months before any problems are caught and rectified.
In addition, standard practice does not facilitate rapid advancement of image processing technologies. Many image processing applications are highly statistical. Therefore large amounts of data must be collected before reasonable conclusions can be drawn. This is particularly true in image processing applications concerning biological specimens where measured features characterizing objects in biological specimens such as, for example, area, perimeter, frequency content, and optical density, among others, vary along a continuum. The standard deviation of any particular feature may be quite large. Further, to obtain accurate, repeatable, and reliable diagnosis of a biological specimen, hundreds of features may be measured for each object in an image. This is further compounded by the fact that thousands of specimens must be evaluated to determine the efficacy of a device. Therefore, image and object feature calculations are taken over the time frame of months. The problem in advancement of image processing technology partially lies in the fact that it is very difficult to know exactly how various system parameters, such as illumination uniformity or image collection frequency response, affect the outcome of various feature calculations. Since standard practice only allows for the measurement of system parameters over long time periods, on the order of months, and requires taking systems scheduled for maintenance out of productivity, to use stand alone equipment, it is nearly impossible to determine system performance at any specific time between maintenance periods. Such uncertainty makes relating system parameters to feature calculation results nearly impossible.
The present invention, for the first time, serves to substantially reduce such reliability problems by providing an automated, on line test to quickly identify any unacceptable deviations in system performance. The apparatus and method of the invention allows formulation of more reliable relationships between system parameters and feature calculations.
The present invention provides an automated method to perform such tests. In accordance with the novel aspects of the invention, testing is done using a test slide containing calibrated clear areas and image primitives that stimulate components of the automated biological specimen analysis system to produce a quantifiable effect. The quantifiable effects are compared to system specifications to ensure that the automated biological specimen system is operating within its engineered limits. If the automated biological specimen analysis system fails to meet these limits, appropriate action is taken. Failing a test may result, for example, in invalidation of all slide results produced since the last acceptable system integrity testing sequence.